Machine learning (ML) can be described as an application of artificial intelligence (AI), in which a ML model is built using algorithms that iteratively learn from training data. Training data can be described as known data points that include patterns, which the resulting ML model should predict. An example ML model can include, without limitation, a classifier that receives input data (e.g., an image), and assigns the input data to one or more classes (e.g., human, cat, dog, based on content of the image). Various techniques can be used to train an ML model.
An example training technique includes supervised learning, in which the training data is labeled, and the labeled training data is processed (e.g., using linear regression) to infer the ML model. However, to implement supervised learning, the training data, which may require relatively large data sets, must be accurately labeled. This can be a daunting, time-consuming, and resource intensive task, which requires a significant level of domain knowledge (e.g., labeling a drug with one or more conditions that the drug treats).